Last updated | August 21, 2025

Indicator information

Name

Land Cover and Land Cover Change statistics

Unit

Land cover (classes and change) statistics are expressed in km² and percentages of the area of interest. 

Area of interest

The land cover classes and land cover change statistics have been generated for each country, ecoregion and terrestrial or coastal protected area. Statistics are provided in the KCBD - Global Biodiversity Data Viewer (GBDV) at country level and through REST Services at country, ecoregion and protected area level.

Related targets

Sustainable Development Goal 15 on life on land

Policy question

How well are different ecosystem types, as indicated by land cover, preserved and how strong are anthropogenic changes affecting their distribution in a given area? Human pressures are constantly increasing and it is important to monitor the consequences of the associated changes on the environment, in particular inside and around protected areas to ensure that natural ecosystems and their associated species and ecosystem functions (e.g. goods and services) are preserved. By comparing land cover maps over time at the country, ecoregion and protected area level, land use changes can be identified. 

Use and interpretation

Land cover is defined as the physical material at the surface of the earth, usually documented via the interpretation of earth observations. Common land cover types include trees, grass, bare ground, built up areas, water, etc.

The land cover maps used here are:

1. The Copernicus Global 100m Land Cover (CGLC) map for the baseline year 2019, providing land cover data using 23 classes and with an overall accuracy of 80% (Buchhorn, M., et al 2020).

2. The maps from the Climate Change Initiative – Land Cover (CCI-LC) project which delivers consistent global maps at 300 m spatial resolution on an annual basis since 1992. For six epochs with an interval of 5 years (1995, 2000, 2005, 2010, 2015, 2020), statistics of land cover classes are computed using 3 aggregation levels to ease visualization of the main trends: the lowest aggregation level (3) corresponds to the original 22 classes. The second one (level 2) shows 14 classes and the last one (level 1) covers only 4 classes. See Table 1 for details.

We also display the transitions between classes which occurred from 1995 to 2020, based on Climate Change Initiative – Land Cover (CCI-LC) data. Understanding whether grasslands or forests are converted into cropland or built up areas is essential to identify the land cover types that are most affected but also to understand the potential drivers between these changes (see e.g. Sanchez-Azofeifa et al., 2003; Beresford et al., 2013; Brink et al., 2016).

End-users of the KCBD Global Biodiversity Data Viewer might sometimes detect significant differences between these statistics and those provided for the changes in surface water and/or for the changes in forest cover. These differences can be due to the use of different imagery, resolutions and methodologies. The changes reported by the specialized services on surface water and forest cover (see relevant factsheets) should be preferred over those derived from the global land cover maps discussed here. 

Key caveats

Since land cover data are derived from earth observations, uncertainties and accuracy in the land cover classification varies in space and time. Clouds are often obstructing observations in tropical regions and coastal areas, and vary a lot from year to year. Because land cover change affecting areas smaller than 1 km² will remain unnoticed, only, change statistics for small protected areas will have to be interpreted with more caution. Different sensors have also been used over time and the older yearly land cover maps are less reliable than the most recent ones. Still, because we use a time interval of 25 years, the main trends in land cover change are expected to be captured, especially if changes occur clearly between the aggregated classes. We refer to the documentation of the land cover CCI product (ESA, 2021 and Land Cover CCI, 2017) for a detailed discussion about the main limitations of the product.

Statistics computed at protected area level will be affected by the accuracy of each protected area boundaries.

Indicator status

Published in peer reviewed papers and technical reports (see References).

Available data and resources

Data

The following Land cover statistics are available in the GBDV for each country:

• From Copernicus LC: year 2019 at 100m resolution, using the second level aggregation;

• Land cover change (transitions between classes from 1995 to 2020, using the first level of aggregation).

The above-mentioned statistics as well as statistics for individual years from ESA-CCI (years 1995, 2000, 2005, 2010, 2015 and 2020 at 300 m resolution using three different levels of aggregation) are also available through REST services for all protected area at least as large as 1 km², for each country and for each terrestrial ecoregion.

Update frequency

Planned annually.

Code

The procedure for the computation of the indicator, which currently involves the use of a wide range of software to handle the different steps, is documented in Juffe Bignoli et al. (2024).

Methodology

Following the methodology described in Juffe Bignoli et al. (2024) for categorical raster datasets, each land cover map has been overlaid with countries, ecoregions and all protected areas. UNESCO Biosphere Reserves have been discarded as well as protected areas recorded only as points. Raw statistics have been computed on original Land Cover categories. For ESA - CCI Land Cover maps, raw statistics have been post-processed and aggregated at three levels of aggregation (4, 14 and 22 land cover classes, respectively) corresponding to an increased level of detail, as shown in Table 1 below. Land Cover Change (1995-2020) is reported for aggregation level 1 only.

For the time series (ESA-CCI product), each pixel of 300 m within the protected area, the country and the ecoregion, the land cover type has been stored for the years 1995 and 2020 to allow the detection of changes between classes from the same product over these years.

Input datasets

References

Beresford, A. E., et al. (2013). Protection reduces loss of natural land-cover at sites of conservation importance across Africa. PLoS ONE, 8: e65370. https://dx.doi.org/10.1371/journal.pone.0065370

Brink, A., et al. (2016). Indicators for assessing habitat values, pressures and threats for protected areas – an integrated habitat and land cover change approach for the Udzungwa Mountains National Park in Tanzania. Remote Sensing, 8(10), 862. http://dx.doi.org/10.3390/rs8100862

Buchhorn, M.; Smets, B.;Bertels, L.; De Roo, B.;Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A.(2020): Copernicus Global Land Service: Land Cover 100m: Version 3Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; http://dx.doi.org/10.5281/zenodo.3938963

Dinerstein et al. (2017), An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm, BioScience, Volume 67, Issue 6, June 2017, Pages 534–545, https://doi.org/10.1093/biosci/bix014

ESA (2021). ESA Land Cover CCI – Product User Guide and Specification. ICDR Land Cover 2016-2020 – v2.1.1. Available online at https://cds.climate.copernicus.eu/datasets/satellite-land-cover

Juffe-Bignoli et al. (2024) Delivering Systematic and Repeatable Area-Based Conservation Assessments: From Global to Local Scales. Land 2024, 13, 1506. https://doi.org/10.3390/land13091506

Land Cover CCI (2017). Product User Guide Version 2.0 http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf.

Lázaro, C., Mandrici, A., Delli, G., Caudullo, G., Bourgoin, C. et al., Challenges in integrating global environmental data with GISCO administrative layers – A GIS perspective, Publications Office of the European Union, 2025. https://dx.doi.org/10.2760/8183010

Sanchez-Azofeifa, G. A., et al. (2003). Integrity and isolation of Costa Rica's national parks and biological reserves: Examining the dynamics of land-cover change. Biological Conservation, 109: 123-135. https://doi.org/10.1016/S0006-3207(02)00145-3

UNEP-WCMC & IUCN (2025). Protected Planet: The World Database on Protected Areas (WDPA) [On-line], [January/2025], Cambridge, UK: UNEP-WCMC and IUCN. www.protectedplanet.net